Some thoughts on Autonomous Vehicles

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Overview

We started this class with a lot of questions an not many answers. We end the class in much the same way. Maybe even with more questions. But thats the nature of a class like this, where we discuss topics that can only be speculated on. We all did certainly learn a lot though.

From class, the most valuable source of information we had was the first hand accounts from the people who are driving this industry and hoping to be driven by this industry. We had experts from the field come in and tell us about their companies contributions to the field. We got to view a little window into the world of people who work on these vehicles and systems every day. We also got to talk to someone who was unable to drive themselves and understand how much self driving cars would affect disabled peoples’ day to day.

We also learned from other elements of class. The discussions brought everyone’s knowledge to the table and created a collective knowledge base. The presentations allows me and my group to become experts in the algorithms driving these cars, both in terms of technical elements and social issues. Our blogs allowed us to explore topics that interested us. My blogs taught me quite a bit about a number of topics.

Blog Summaries

My first blog explored the idea of open source software and how it relates to autonomous vehicles. Most companies in the space currently use proprietary software, which is more secure and gives them competitive advantages over other companies in the space. On the other hand, open source software is a way to share information between companies to improve algorithms. It would also increase transparency and pave the way for a standard safety algorithm to drive these cars that is agreed upon in some way by the companies or government.

My second blog focused on the feasibility of using self driving technology in big rig trucks. A lot of companies are trying to tackle the challenge, including Tesla, but there are some pressing issues to take of first. Truck drivers provide maintenance and theft prevention to their trucks and cargo. There is also a huge ethical problem to self driving trucks in that trucking is one of the biggest occupations in the United States, and eliminating these jobs would have social consequences for these newly unemployed people. Some of these issues are being considered, but there may be problems if some are ignored.

My third blog talked about the basics of algorithms, which are the brains behind the self driving cars on the road today. Algorithms in self driving cars are almost always based on the concept of Machine Learning, a mathematical and statistical algorithmic method that uses massive amounts of data to recognize situations and make decisions. There are some interesting issues surrounding the use of these algorithms in the self driving space, such as the amount of trust and responsibility put on these algorithms.

My fourth post looked at the trends of journalism and press about these autonomous vehicles. There is a trend in public opinion towards being scared of new technology that is certainly present in the public’s opinion of self driving cars. The big problem with this knee-jerk reaction is that people will look for information that confirms their biases, so people voicing their worries makes more people worried, which starts a vicious cycle. Some journalists use this bias-searching to gain more views on their articles.

My fifth blog explored how green electric vehicles really are. Since most autonomous vehicles are electric and tout their green-ness, its important to examine their claims. According to most research, it is better to buy a used internal-combustion vehicle than a new electric vehicle because of the huge environmental cost of production of a vehicle and its metal-laden battery. Those huge batteries have a relatively short life compared to a normal car engine, and must be disposed of in very particular ways. There is also the issue that the electricity driving these cars is only as green as where it was generated.

Future Directions

We all learned a lot about something that’s coming soon but is still very much in development. We started this class by asking when we thought self-driving cars were coming. We have since talked our heads off about this question. My opinion has not changed but instead been solidified by the information we learned during this class. Level 5 autonomous vehicles will not be on our roads anytime soon, but Level 2 and 3 vehicles are already here and will become more and more common. I think that the problem of transitioning will prevent any Level 4 vehicles from becoming widely popular. My best guess is 20 to 30 years until Level 5 is commonplace.

The wonder of this subject is that there are new developments and technologies coming out ever day. There’s no way to truly predict what will happen in the space, so lets all just buckle up for the ride!

Links

Here are some of my favorite sources I found throughout my research for this class.

Most autonomous cars are designed with as much fancy new space-age technology as can be packed in to showcase the many advances in comfort, efficiency, and technology. One feature that has made it to most autonomous vehicles is electric motors. This technology is touted to be more efficient and better for the environment than traditional, internal-combustion engines. But is it really?

Let’s take Tesla for example. Tesla is one of the first consumer-available cars to make semi-autonomous driving accessible and usable. Tesla also produces only electric vehicles, including and electric semi-truck and a electric sports car. Tesla’s flagship vehicle is the Model S. The Model S uses a half ton lithium ion battery pack filled with rare earth metals that are incredibly costly to mine. These metals are also sourced from around the world and must be shipped across the globe from places such as the Democratic Republic of the Congo and China. All this means that creating the batteries for these vehicles have a huge environmental impact. Experts estimate that it takes “113 million BTUs of energy to make a Toyota Prius. Because there are about 113,000 BTUs of energy in a gallon of gasoline, the Prius has consumed the equivalent of 1,000 gallons of gasoline before it reaches the showroom.”

The grand advantage of electric vehicles is that once on the road, the vehicles is powered by electricity as opposed to the fossil fuel gasoline or diesel. This is all well and good if the electricity is generated through green-energy methods such as wind or solar. But often, the energy is being generated through methods comparably as bad as gasoline, such as coal. This essentially nullifies the advantage of being powered by electricity over gasoline.

Then there is the problem of disposing of these batteries when the electric vehicles has reached the end of its lifetime. Tesla and other automakers have programs addressing this problem, but these batteries are hard and costly to recycle. There is a huge problem of e-waste being sent off to third-world countries to be salvaged for scraps that is not addressed in our modern materialistic society.

Electric vehicles may be advantageous in some ways, but there are definite drawbacks as well. Autonomous vehicle manufacturers should take a good hard look at what is actually best for the environment when deciding how their vehicles will be powered.

Over the course of this course (pun intended), we have all read countless news articles about self driving cars. Some are more technical, some are informational, some are opinion. There are a lot of articles in support of these vehicles, but there are also mountains of writers trashing self-driving cars. There are articles that trash the safety, the implementation, the user experience, and even the basic premise of self driving cars.

So why the hate? Simply put, new things can be scary. Why? Because of risk, as shown in this study. We as beings that want to survive generally avoid risk, which diminishes the likelihood of us surviving. New things generally have a larger risk factor, and definitely have a more unknown risk factor. So our survival instincts are telling us to avoid this new technology that is scary, like how people were afraid of computers in 1995.

These self-driving cars activate fear in a lot of people. And these people look for headlines that confirm their bias, an action that can be attributed to confirmation bias, a concept in psychology that describes how people are likely to search out and believe information that confirms their biases. So its advantageous for a writer to publish an article that talks about and enforces the views of these people. Fear makes people devour these articles, since self-preservation is one of the strongest human impluses.

This is kinda screwed up in my mind. This is why articles like this one gain traction. This article is a perfect example of how some writers will write slanderous headlines that make self driving cars the enemy, then describe in an article how the headline is essentially wrong. These headlines can influence people who only read the headline unfairly, as stated in this article.

This kind of journalism generates views and clicks for writers and papers, but is damaging to the industry of self driving cars. I admit that I have a bias in favor of these vehicles, but I feel that many articles against them are preying on our fears to generate capital, thus presenting an unfair view of the situation and swaying public opinion negatively.

So what makes these cars that we are spending a semester studying go? An engine. But an algorithm is telling the car when to go. And when to stop, and turn, and park, and merge, etc, etc.

An algorithm is a process or set of rules to be followed in calculations or other problem-solving operations. Basically, an algorithm is a set of step-by-step instructions for solving a problem. There are simple algorithms, like how to tie a shoelace, to very complex algorithms, like those that are used to manage stocks for investors.

The algorithms driving self-driving cars are incredibly complex. They take inputs from a variety of sensors and recognize the objects, such as cars, pedestrians, and road signs. The objects are then compared to the a library of sorts that the computer has learned from Machine Learning and its actions are predicted. The computer then takes all this information and decides the best course of action for the car. This is done many times every second to ensure total control of the situation.

These algorithms are incredibly powerful and are constantly being researched and updated, but there is always the possibility of a situation arising that the car is unprepared for. So what should be done when an algorithm causes an accident or fails? Is the car manufacturer at fault? Or the coder? Or do we just add this edge case to the system and move on?

Another potential problem with these algorithms is the issue of power and control. By putting our trust in these algorithms, our society is putting our trust into a single entity, as opposed into the million collective minds of our own. This collection of power can have social consequences, such as when Facebook’s censorship algorithms curated the news.

These algorithms are incredibly complex and well executed. But we are putting a lot of trust into something created by our flawed selves, so we should make sure that we are creating something better than ourselves.

One area of the more controversial topics in autonomous vehicles is autonomous trucks. Emotions towards the subject range from excitement to fear, and predictions for their practical arrival date range from a couple months to decades.

Self-driving trucks could change the industry of long-range trucking. Currently, labor costs are huge for the monotonous job of trucking across hundreds of miles a day. Autonomous trucking could reduce labor costs and increase safety for other drivers.

There are a number of problems with the concept though. Firstly, the safety of letting an AI handle something as complex as trucking is questionable. Because of trucks’ large size, there are so many potential variables for these vehicles.

Another concern is the maintenance problem. Truckers are currently paid more if they are able to service their own vehicles, because this means more time on the road, which is more money to the trucking company. A truck without a driver would be unable to perform this type of maintenance.

For a completely autonomous truck, another problem is that of theft-prevention. As detailed by a Reddit user, truckers are currently used as theft-prevention for their cargo (this user also bring up a number of problems not addressed here). An autonomous truck would be easier to hijack or steal from with current systems, especially considering the cyber-security problem.

Finally, there is a huge ethical concern, as trucking is huge occupation in the United States. What is to be done with the truckers suddenly forced out of their jobs by automation? The average trucker does not have many skills beyond trucking and truck maintenance. What kind of employment opportunities will arise for this suddenly job-less workforce?

There are some halfway solutions, though. Daimler is working on caravanning technology, which would employ a human as a lead driver and autonomous truck drivers as followers. Many have theorized that autonomous trucks could handle the long interstate drives while humans handle city driving. Maybe a human could always be in the cab as a monitor, but only drive in certain situations, similar to the current autonomous capabilities of Teslas?

These problems will all be tackled in the coming years by various titans and newcomers to the industry. I would caution those that expect trucking to disappear as an occupation that their dream may come true, but not anytime soon.

A particularly interesting subject in the world of autonomous cars is the open source status of the algorithms driving these cars.

The Open-Source model is a model for developing software wherein the code is available to the public. The idea is that this freedom to view the code will encourage three things: (1) an increased knowledge base in the public in terms of code, (2) improved code because improvements can essentially be crowdsourced, and (3) more trust in the code, since nothing nefarious can be hidden. This idea is so popular there are entire organizations dedicated to it, like the Open Source Initiative.

This kind of sharing could do wonders for autonomous vehicles. The public would trust the vehicles more and the code could constantly be re-examined. Each manufacturer could share their safety technology, like Volvo did with the seatbelt, as opposed to hiding it away and risking public safety.

There are risks of course. Open-source software introduces safety concerns and can lead to confusion in the public over a necessary line of code that can seem nefarious. Certain procedures would have to be hidden, such as update procedures, to reduce hackability.

Overall, open-source is something the car industry should consider for some, but not all, of its algorithms related to self-driving cars.

So why am I taking this class, Autonomous Vehicles? I could be taking an easy elective and relaxing my senior year, but instead I’m in this class to learn about what I think will be the greatest change in day-to-day life that my generation will see.

Autonomous vehicles have been in the public eye a lot recently, but they’ve been in the minds of scientists and engineers since 1939, when General Motors put on a World Fair to show the public what the company thought the future would look. Part of this exhibit was a concept of a car that could drive itself. (Source)

Since then engineers have been working towards making this concept closer to reality. Advances in sensing technology, algorithms, and safety have all brought this ideal closer to reality. And recently, the science and tech has gotten close enough that the possibility of self-driving cars hitting the road is very real.

At this moment, Google, Uber, Tesla, and many others are testing self-driving cars. Others are tackling self-driving long range semis. The world is on the edge of the tech necessary, and now it is time to figure out everything else. And that’s a big everything.

Infrastructure, laws, training, attitudes, and more need to change to make this dream a reality. Its going to take effort from nearly every sector to bring these cars to the consumer. The field is exploding with jobs, and I’m interested in every single aspect.

To answer my question, I’m interested in this class because it feels like the “flying cars” of my generation. Except it will actually happen. Now is such a unique time to be alive that I want to absorb all I can about now so I can be best poised to do well in the future.